Given a query image such as a query image of a human face, content-based face image retrieval (CBIR) tries to find similar face images from a large image database. Such face retrieval systems have many applications, such as automatic face annotation, missing person search, suspect search, surveillance, etc.
Traditional methods for object image retrieval usually use low level features to represent features of the object, such as low level features to represent faces. However, low level features lack semantic meaning (e.g., different people look similar) and face images usually have high intra-class variance (e.g., different face images of the same person have different expressions and poses). Recently, there has been some research to handle these problems. See Chen [33] and Wu [20].